Knowing the precise geographic location (“geolocation”) of a merchant can be valuable for a variety of reasons. Most notably, mapping applications on consumer computing devices may often require geolocation information for merchants in order to both accurately draw maps of geographic areas and provide GPS-assisted navigation information to the consumer and other users. Inaccurate geolocations for merchants may therefore lead to inaccurate maps and inaccurate navigation directions provided to users, which could be damaging to a service's reputation. As a result, mapping and navigation services may desire updated and accurate merchant geolocations to improve their services.
However, many current methods for identifying merchant geolocations are often too error-prone, time consuming, and/or inefficient. For example, one method for identifying merchant geolocations includes having an employee physically visit merchant locations and identify the geographic coordinates, or other suitable representation, of the merchant location. However, this can require a vast amount of resources and time, especially on a large scale, and can be inaccurate without obtaining multiple measurements at each merchant. In another example, merchants may self-report their geolocation to the service. However, the gathering of such data may be time consuming, and may also be inaccurate as it relies on the merchants to self-report geolocation information, who may not provide proper or accurate data.
Many consumers that shop at various merchants often are in possession of a mobile computing device that is configured to report its geolocation. As such, the geolocation data of these mobile computing devices may be ideal for inferring a merchant geolocation. Thus, there is a need for a technical solution to infer merchant geolocations using mobile computing device geolocation data in combination with transaction data, while still maintaining a high level of privacy for consumers and other users of the mobile computing devices.